Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy

Working Paper No. 22124 Can open tournaments improve the quality of city services? The proliferation of big data makes it possible to use predictive analytics to better target services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction alg...

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Bibliographic Details
Published in:NBER Working Paper Series 2016-03, p.22124
Main Authors: Glaeser, Edward L, Hillis, Andrew, Scott Duke Kominers, Luca, Michael
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Big Data
Cities
Collaboration
Crowdsourcing
Economic theory
Government
Hygiene
Inspections
Opportunity costs
Outsourcing
Predictive analytics
Restaurants
Scientists
Tournaments & championships
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Summary:Working Paper No. 22124 Can open tournaments improve the quality of city services? The proliferation of big data makes it possible to use predictive analytics to better target services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to crowdsource competence by making data public and offering a reward for the best algorithm. This paper provides a simple model suggesting that open tournaments dominate consulting contracts when cities have a reasonable tolerance for risk and when there is enough labor with low opportunity costs of time. We also illustrate how tournaments can be successful, by reporting on a Boston-based restaurant hygiene prediction tournament that we helped coordinate. The Boston tournament yielded algorithms--at low cost--that proved reasonably accurate when tested "out-of-sample" on hygiene inspections occurring after the algorithms were submitted. We draw upon our experience in working with Boston to provide practical suggestions for governments and other organizations seeking to run prediction tournaments in the future.
ISSN:0898-2937
DOI:10.3386/w22124